EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions

The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed. Document type: Articl

Exploring the Role of Cities in Electrifying Passenger Transportation

Key Takeaways1. The electrification of passenger vehicles should be one part of a city’s transportation plan. Shifting from internal combustion engine vehicles to plug-in electric vehicles (PEVs) can improve urban air quality, reduce greenhouse gas emissions, and reduce energy consumption.2. Recent studies show that electric vehicle awareness is low even in mature markets; cities should promote electric vehicles to residents by leveraging existing promotional campaigns.3. Various financial and non-financial incentives can effectively encourage electric vehicle uptake, including: free, discounted, or preferential-location parking; free or reduced road and bridge tolls; and allowing electric vehicles to drive in bus or carpool lanes.4. Several cities are restricting or planning to restrict the access that internal combustion engine vehicles (ICEVs) have to certain areas. If these restrictions apply to most (or all) passenger ICEVs, they can promote PEV purchase and use in cities.5. Infrastructure development in cities should follow the same fundamental approach as that used outside of cities. The priority should be ensuring that PEV owners and prospective PEV buyers have access to charging at or near home. Workplace and public charging should be developed for those who cannot access charging at or near home.6. Cities should be strategic in their approach, first identifying the goals they want to achieve, and then exploring what steps they can take to meet these goals. The steps available will likely differ between cities due to the different ways in which roads, parking, and any other vehicle infrastructure is governed

Exploring the Role of Plug-In Hybrid Electric Vehicles in Electrifying Passenger Transportation

Key Takeaways1. Plug-in hybrid electric vehicles (PHEVs) have an important role in the electrifi cation of passenger transportation. Long-range PHEVs not only are a transitional technology. They also are an enabling technology that can encourage more consumers to adopt electric vehicles.2. The electric range of PHEVs has a signifi cant impact on electric vehicle miles traveled. PHEVs with electric range of at least 60km (37 miles (EPA Range)) have a similar ability to electrify travel as short-range battery electric vehicles (BEVs).3. Assuming the goal of policymakers is to increase electric vehicle miles traveled, policy support should correspond directly to electric driving range of both PHEVs and BEVs. Short-range PHEVs should receive less policy support; long-range PHEVs and BEVs should receive more policy support.4. Consumer research in several countries shows that mainstream consumers tend to be more attracted to PHEVs than to BEVs, however many consumers are unaware of how a PHEV diff ers from a BEV. Consumers and car dealerships need to be educated about PHEVs, their benefi ts, and the importance of charging the vehicles

A review of consumer preferences of and interactions with electric vehicle charging infrastructure

This paper presents a literature review of studies that investigate infrastructure needs to support the market introduction of plug-in electric vehicles (PEVs). It focuses on literature relating to consumer preferences for charging infrastructure, and how consumers interact with and use this infrastructure. This includes studies that use questionnaire surveys, interviews, modelling, GPS data from vehicles, and data from electric vehicle charging equipment. These studies indicate that the most important location for PEV charging is at home, followed by work, and then public locations. Studies have found that more effort is needed to ensure consumers have easy access to PEV charging and that charging at home, work, or public locations should not be free of cost. Research indicates that PEV charging will not impact electricity grids on the short term, however charging may need to be managed when the vehicles are deployed in greater numbers. In some areas of study the literature is not sufficiently mature to draw any conclusions from. More research is especially needed to determine how much infrastructure is needed to support the roll out of PEVs. This paper ends with policy implications and suggests avenues of future research

Characteristics of Dutch EV drivers

There has been little research towards the characteristics of Dutch electric vehicle (EV) drivers. This paper describes the result of focus groups and a survey with 286 respondents. Results indicate the current EV driver is predominantly a well-educated middle aged male with a high paying job. We link this to the price of the vehicles and the structure of tax incentives. He is moderately environmentally friendly and likes innovation. He loves the driving experience and promotes EVs towards friends. Most EV drivers are unsatisfied with their all electric range and on average a range of 375 km is desired. Bigger batteries will lead to better use of charging infrastructure. Fast charging is considered important for longer trips but not as a replacement for chargers at parking locations. Smart charging is well received - as long as the user stays in control - which bodes well for the synergy between EVs and the renewable electricity grid.</p

Characteristics of Dutch EV drivers

There has been little research towards the characteristics of Dutch electric vehicle (EV) drivers. This paper describes the result of focus groups and a survey with 286 respondents. Results indicate the current EV driver is predominantly a well-educated middle aged male with a high paying job. We link this to the price of the vehicles and the structure of tax incentives. He is moderately environmentally friendly and likes innovation. He loves the driving experience and promotes EVs towards friends. Most EV drivers are unsatisfied with their all electric range and on average a range of 375 km is desired. Bigger batteries will lead to better use of charging infrastructure. Fast charging is considered important for longer trips but not as a replacement for chargers at parking locations. Smart charging is well received - as long as the user stays in control - which bodes well for the synergy between EVs and the renewable electricity grid

Characteristics of Dutch EV drivers

\u3cp\u3eThere has been little research towards the characteristics of Dutch electric vehicle (EV) drivers. This paper describes the result of focus groups and a survey with 286 respondents. Results indicate the current EV driver is predominantly a well-educated middle aged male with a high paying job. We link this to the price of the vehicles and the structure of tax incentives. He is moderately environmentally friendly and likes innovation. He loves the driving experience and promotes EVs towards friends. Most EV drivers are unsatisfied with their all electric range and on average a range of 375 km is desired. Bigger batteries will lead to better use of charging infrastructure. Fast charging is considered important for longer trips but not as a replacement for chargers at parking locations. Smart charging is well received - as long as the user stays in control - which bodes well for the synergy between EVs and the renewable electricity grid.\u3c/p\u3

EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions

The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed

EV idle time estimation on charging infrastructure, comparing supervised machine learning regressions

The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.JRC.C.3-Energy Security, Distribution and Market

Using electric vehicles as flexible resource in power systems: A case study in the Netherlands

peer reviewedThe European Union (EU) is actively encouraging member countries to phase out traditional oil-fuelled cars in cities in favour of Electric Vehicles (EV) as a solution for increasing efficiency, contributing to ensure security of supply, decrease CO2 emission and decrease local (especially urban) air pollution coming from the transport sector. With the increasing associated charging infrastructure deployment all over European countries, the demand coming from these technologies might impact the power system and potentially offer a source of flexibility through variable charging profiles and battery storage capacity options. In the recent years, the Dutch government has adopted several policy support mechanisms for EVs uptake, which made the Netherlands the country with the highest market share of electric cars in 2015 in the EU, and the second-highest share worldwide after Norway. This country is therefore a unique case study for the analysis of EVs charging demand and impact of the power sector. The recent uptake of EVs provides a significant amount of historical data regarding charging profiles, connection times or utilisation patterns pertaining this technology. This paper aims at exploiting such data to evaluate the current and future impact of electric vehicle deployment on the power system. To that end, historical data from 2015 are used in conjunction with the Dispa-SET model, a unit commitment and power dispatch model developed at the Joint Research Centre (JRC) of the European Commission. This aims at investigating the impact of Battery Electric Vehicle (BEV) charging demand on the current Dutch power system, under different hypotheses for BEV technology penetration and renewable energy deployment (i.e. wind and solar) in the country. In addition, looking at the entire connection periods when vehicles are plugged to the grid, the battery capacity connected to the system is estimated, based on commercial BEV battery characteristics. This total capacity is finally used as variable storage unit in the model, in order to investigate the impact that Vehicle to Grid (V2G) strategies could have on the optimal use of power resources available, with particular attention to Variable Renewable Energy (VRE)